There exist a number of satellites on different earth observation platforms, which provide multispectral images together with a panchromatic image, that is, an image containing reflectance data representative of a wide range of bands and wavelengths. Pansharpening is a pixel-level fusion technique used to increase the spatial resolution of the multispectral image while simultaneously preserving its spectral information. In this paper, we provide a review of the pan-sharpening methods proposed in the literature giving a clear classification of them and a description of their main characteristics. Finally, we analyze how the quality of the pansharpened images can be assessed both visually and quantitatively and examine the different quality measures proposed for that purpose.

In this paper, we propose the application of the hierarchical Bayesian paradigm to the image restoration problem. We derive expressions for the iterative evaluation of the two hyperparameters applying the evidence and maximum a posteriori (MAP) analysis within the hierarchical Bayesian paradigm. We show analytically that the analysis provided by the evidence approach is more realistic and appropriate than the MAP approach for the image restoration problem. We furthermore study the relationship between the evidence and an iterative approach resulting from the set theoretic regularization approach for estimating the two hyperparameters, or their ratio, defined as the regularization parameter. Finally the proposed algorithms are tested experimentally.

Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the image and blur, and gamma distributions for the unknown parameters (hyperparameters) of the priors and the image formation noise. We show how the gamma distributions on the unknown hyperparameters can be used to prevent the proposed blind deconvolution method from converging to undesirable image and blur estimates and also how these distributions can be inferred in realistic situations. We apply variational methods to approximate the posterior probability of the unknown image, blur, and hyperparameters and propose two different approximations of the posterior distribution. One of these approximations coincides with a classical blind deconvolution method. The proposed algorithms are tested experimentally and compared with existing blind deconvolution methods.

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Context. Low surface brightness galaxies (LSBGs) represent a significant percentage of local galaxies but their formation and evolution remain elusive. They may hold crucial information for our understanding of many key issues (i.e., census of baryonic and dark matter, star formation in the low density regime, mass function). The most massive examples -the so called giant LSBGs -can be as massive as the Milky Way, but with this mass being distributed in a much larger disk. Aims. Malin 1 is an iconic giant LSBG -perhaps the largest disk galaxy known. We attempt to bring new insights on its structure and evolution on the basis of new images covering a wide range in wavelength. Methods. We have computed surface brightness profiles (and average surface brightnesses in 16 regions of interest), in six photometric bands (FUV, NUV, u, g, i, z). We compared these data to various models, testing a variety of assumptions concerning the formation and evolution of Malin 1. Results. We find that the surface brightness and color profiles can be reproduced by a long and quiet star-formation history due to the low surface density; no significant event, such as a collision, is necessary. Such quiet star formation across the giant disk is obtained in a disk model calibrated for the Milky Way, but with an angular momentum approximately 20 times larger. Signs of small variations of the star-formation history are indicated by the diversity of ages found when different regions within the galaxy are intercompared. Conclusions. For the first time, panchromatic images of Malin 1 are used to constrain the stellar populations and the history of this iconic example among giant LSBGs. Based on our model, the extreme disk of Malin 1 is found to have a long history of relatively low star formation (about 2 M yr −1 ). Our model allows us to make predictions on its stellar mass and metallicity.

Super-resolution algorithms recover high-frequency information from a sequence of low-resolution observations. In this paper, we consider the impact of video compression on the super-resolution task. Hybrid motion-compensation and transform coding schemes are the focus, as these methods provide observations of the underlying displacement values as well as a variable noise process. We utilize the Bayesian framework to incorporate this information and fuse the super-resolution and post-processing problems. A tractable solution is defined, and relationships between algorithm parameters and information in the compressed bitstream are established. The association between resolution recovery and compression ratio is also explored. Simulations illustrate the performance of the procedure with both synthetic and nonsynthetic sequences.

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In this paper we present a super resolution Bayesian methodology for pansharp-ening of multispectral images. By following the hierarchical Bayesian framework, and by applying variational methods to approximate probability distributions this methodology is able to: a) incorporate prior knowledge on the expected characteristics of the multispectral images, b) use the sensor characteristics to model the observation process of both panchromatic and multispectral images, c) include information on the unknown parameters in the model in the form of hyperprior distributions, and d) estimate the parameters of the hyperprior distributions on the unknown parameters together with the unknown parameters, and the high resolution multispectral image. Using real data, the pansharpened multispectral images are compared with the images obtained by other parsharpening methods and their quality is assessed both qualitatively and quantitatively.